Learning Outcomes
Participants will learn advanced concepts, their theoretical foundation, and the most common models
of stochastic processes used in computational neuroscience to model complex noisy neural
systems. Participants will learn simulation techniques and how to evaluate simulation output. Participants
will learn to adapt models to new problems as well as to develop new models of neural systems.
Content
This module provides advanced knowledge about the mathematical modeling, analysis and numerical
simulation of complex neural systems under the influence of noise using stochastic processes.
Specific topics addressed are: coupling of neurons, cooperative phenomena in neural networks, in
particular in stochastic reaction diffusion models (synchronization, waves), continuum limits of neural
networks (strong and weak coupling, reaction diffusion systems and neural field equations)
